Hadoop Vs Conventional Databases – Which One to Choose?

Today’s ultra-connected globe is actually producing enormous amounts of data at high rates. Subsequently, big data analytics has turned into a highly effective tool for organizations aiming to leverage piles of precious data for higher revenue and competitive benefit. Amid this big data dash, Hadoop, being a cloud-based system continues to be intensely marketed as the perfect solution for the big data issues in business world. While Hadoop has existed around much of the buzz, there are specific circumstances in which running workloads over a conventional database would be the superior solution.

For businesses wanting to know which features will better assist their big data requirements, below are a few important questions that should be asked when selecting Hadoop – which includes cloud-based Hadoop – or a conventional database.

Is your data structured or unstructured?Structured Data: Data which exists inside the fixed limits of a file is called structured data. Since the structured data can be inserted, saved, queried, and assessed in an easy and simple way, such data is better served with a conventional database.

Unstructured Data: The type of data that emanates from many different sources, like emails, text files, videos, images, audio files, as well as social media sites, is called unstructured data. Being complicated and voluminous, this type of data generally cannot be managed or proficiently queried with a conventional database. Hadoop’s power to join, blend, and assess large amount of unstructured data without structuring it first enables businesses to achieve deeper insights easily. Therefore Hadoop will be the ideal solution for businesses aiming to store and assess huge amounts of unstructured data.

Do you need a scalable infrastructure?

Businesses with constant and predictable data workloads are going to be better suitable for a conventional database.

Organizations challenged by growing data demands may wish to reap the benefits of the scalable infrastructure of Hadoop. Scalability enables servers to support increasing workloads. Being a cloud-based solution, Hadoop provides better flexibility and scalability through spinning the servers within shorter time to accommodate changing workloads.

Will implementing Hadoop remain affordable?

Affordability is actually an issue for businesses seeking to take up new technologies. When it comes to Hadoop implementation, businesses have to do their groundwork to ensure that the recognized advantages of implementing Hadoop outweigh the expenses. Else it’s better to stay with a conventional database to fulfill data management requirements.

With that said, Hadoop has quite a few points taking it which make implementation a lot more affordable than businesses may comprehend. To begin with, Hadoop saves cash by merging open source systems with virtual servers. Hadoop keep costs down even more by reducing the cost of servers and warehouses.

Hybrid systems that assimilate Hadoop with conventional relational databases tend to be gaining interest as affordable ways for businesses to gain the advantages of each platform.

Is fast analysis your requirement?

Hadoop was originally created processing large amount of distributed data that handles every record in the database. Apparently, this kind of processing will take time. For tasks in which fast processing isn’t essential, like reviewing every day orders, checking historical data, or carrying out analytics where a slower analysis can be accepted, Hadoop is suitable.

On the other side, in situations where companies demand faster data analysis, a conventional database would be the better option. That’s due to the reason that quick analysis isn’t about analyzing substantial unstructured data, which can be nicely done with Hadoop. It’s more about analyzing shorter datasets in real time, which is just what conventional databases are nicely outfitted to perform.

Hybrid systems will also be a great fit to think about, since they let businesses make use of conventional databases to run smaller, hugely interactive workloads when employing Hadoop to assess large, complicated data sets.

Which one is better?

That would depend. While big data analytics offer deeper insights providing competitive edge, those advantages may simply be recognized by businesses that work out sufficient research in considering Hadoop as an analytics tool that perfectly serves their requirements.